Following a heart attack or the development of some cardiovascular diseases, the movement of the heart walls during the cardiac cycle may change, which affects the motion of blood through the heart. Currently, valvular blood flow can be monitored using imaging techniques such as Doppler ultrasound and MRI. However, because the spatial resolution of such techniques are low, and it is not possible to observe the detailed interaction of blood flow and the endo-cardial surface of the heart, the formation of a cardiac thrombus remains difficult to predict. If a physician were able to visualize or quantitatively measure the detailed alteration of the blood flow by altered contraction, he/she might be able to make a better diagnosis or treatment plan.
Heart model reconstruction is essential to the simulation results. The detailed cardiac shape features, which are accurately reconstructed from these images can be used as the boundary conditions for a fluid simulator to derive the hemodynamics along the whole heart cycle.
With the rapid development of high-resolution cardiac CT, patient-specific blood flow simulation is quickly becoming one of the central goals in the study of cardiac blood flow. Mihalef et al. (2009) published a framework for simulating atrioventricular blood flow, and showed simulation results using a complete model of the left side of the heart, including the atrial venae and an aortic stub, together with modeled valve kinematics. The geometry used in Mihalef et al. (2009) was obtained from scans based on data from the Visible Human Project, while the kinematics was transferred to the model from MRI data. Later, Mihalef et al. (2010) used smoothed 4D CT data to simulate left ventricular blood flow, and compared the flow around through the aortic valve in a healthy heart and two diseased hearts. The models derived from CT data in Mihalef et al. (2010) were highly smoothed and were not useful for understanding the true interactions between the blood flow and the walls.
Earlier work in blood flow simulation used less refined models. For example, Jones and Metaxas (1998) were the first to extract boundaries from MRI data to perform patient-specific blood flow simulations. Later, Q. Long et al. (2003) and Saber N. (2003) used simple models of the left side of the heart, with smooth ventricular walls, and imposed boundary conditions in the valve regions.
There has also been work on the 3D cardiac reconstruction from CT images Zheng et al. (2008); von Berg and Lorenz (2005); Lorenz and von Berg (2006). However, despite the higher level of structural detail potentially available in CT data, most prior work has not sought to capture the finer detail structures of the myocardium, such as the papillary muscles and the trabeculae. The conventional approach to reconstructing cardiac structures from 3D images (e.g., for generating generic or patient-specific models of the heart) is a model-based one that uses a smooth parametric model to guide the segmentation of cardiac structures from the 3D images. Such parametric models capture the overall shape of the heart wall, but are too coarse to capture or incorporate many of the finer scale anatomical features. Recent advances in CT technology have made the acquisition of higher resolution cardiac images possible, which can capture previously unseen cardiac structure details. Chen et al. proposed a hybrid framework for 3D cardiac reconstruction [Chen et al. 2004]. That method has provided high resolution segmentation results of the complex cardiac structure. Their results captured the papillary muscles and detail structure of the myocardium.
The described invention uses a high resolution heart model by using segmenting higher-quality CT scans of normal subjects and live patients with cardiovascular disease that allow the capture of the complex details of the heart walls and trabeculae. The approach, which estimates the predefined motion for the valves, whose asynchronous opening and closing provides a simple geometric mechanism for taking care of those boundary conditions, relies on the reasonable assumption that the left ventricle drives essentially all of the dynamics of the blood flow in the left side of the heart. The described invention captures the fine detail structures of the myocardium, as well as the one-to-one correspondence between frames, which is required in the blood simulator. With the one-to-one correspondence, one can do interpolation among different time frames to get a smoother heart cycle reconstruction.